Pub Date : 2026-01-10DOI: 10.1016/j.compeleceng.2026.110945
Shasya Shukla, S.K. Jha
This study presents an advanced intelligent control strategy for Load Frequency Control (LFC) in a multi-area hybrid power system (HPS) comprising reheat thermal units, nuclear generation, and renewable energy sources (RESs) such as wind power, supported by a Battery Energy Storage System (BESS). The study proposes a novel HBA-tuned Deep Deterministic Policy Gradient Reinforcement Learning (DDPG-RL) controller designed to enhance dynamic frequency regulation under varying operating conditions. In the proposed approach, a reinforcement learning agent adaptively modulates governor setpoints and coordinates auxiliary energy resources to suppress frequency deviations. To further improve policy convergence and optimization quality, the critical hyperparameters of the agent are fine-tuned using the Honey Badger Algorithm (HBA), a recent nature-inspired metaheuristic based on the foraging intelligence and digging behavior of honey badgers. The hybrid HBA-DDPG framework enables robust adaptation to load fluctuations, renewable intermittency, and inter-area disturbances while maintaining tie-line power balance. Simulation studies demonstrate significant improvements over conventional controllers and standalone metaheuristic-based methods showing settling time (7.6 s.), maximum overshoot (1.4%), and overall error indices (ISE as 0.0022 and ITAE as 0.566) hence highlighting the effectiveness of combining reinforcement learning with metaheuristic optimization, offering a scalable, resilient, and high-performance solution for next-generation smart grids.
{"title":"A hybrid HBA-tuned DDPG reinforcement learning strategy for intelligent load frequency control in multi-area hybrid power systems","authors":"Shasya Shukla, S.K. Jha","doi":"10.1016/j.compeleceng.2026.110945","DOIUrl":"10.1016/j.compeleceng.2026.110945","url":null,"abstract":"<div><div>This study presents an advanced intelligent control strategy for Load Frequency Control (LFC) in a multi-area hybrid power system (HPS) comprising reheat thermal units, nuclear generation, and renewable energy sources (RESs) such as wind power, supported by a Battery Energy Storage System (BESS). The study proposes a novel HBA-tuned Deep Deterministic Policy Gradient Reinforcement Learning (DDPG-RL) controller designed to enhance dynamic frequency regulation under varying operating conditions. In the proposed approach, a reinforcement learning agent adaptively modulates governor setpoints and coordinates auxiliary energy resources to suppress frequency deviations. To further improve policy convergence and optimization quality, the critical hyperparameters of the agent are fine-tuned using the Honey Badger Algorithm (HBA), a recent nature-inspired metaheuristic based on the foraging intelligence and digging behavior of honey badgers. The hybrid HBA-DDPG framework enables robust adaptation to load fluctuations, renewable intermittency, and inter-area disturbances while maintaining tie-line power balance. Simulation studies demonstrate significant improvements over conventional controllers and standalone metaheuristic-based methods showing settling time (7.6 s.), maximum overshoot (1.4%), and overall error indices (ISE as 0.0022 and ITAE as 0.566) hence highlighting the effectiveness of combining reinforcement learning with metaheuristic optimization, offering a scalable, resilient, and high-performance solution for next-generation smart grids.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110945"},"PeriodicalIF":4.9,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-10DOI: 10.1016/j.compeleceng.2026.110949
Rajakumar Ponnumani , Nisha Vasudeva , Thenmozhi Elumalai , Prabu Kaliyaperumal , Balamurugan Balusamy , Francesco Benedetto
The rapid proliferation of Internet of Things (IoT) devices in cloud environments has led to an expanded attack surface and increased susceptibility to diverse and evolving cyber threats. This study proposes a robust, multi-stage hybrid intrusion detection framework designed to address the challenges of high-dimensional data, class imbalance, and dynamic traffic in IoT ecosystems. The framework integrates Variational AutoEncoder (VAE) for latent feature compression, Isolation Forest (IF) for unsupervised anomaly detection, and Graph Attention Network (GAT) for relational modeling and multi-class classification. The CIC IoT-DIAD 2024 dataset is utilized to evaluate performance across multiple attack categories. The VAE extracts compact latent representations, enabling effective anomaly detection through IF. Detected anomalies are then structured into graph topologies, and classified by GAT based on node-level features and inter-node relations. Experimental results demonstrate superior detection performance with an overall accuracy of 99.08% and an F1-score of 98.03%, outperforming traditional and deep learning baselines. The proposed system exhibits strong scalability, generalization, and adaptability to dynamic IoT-cloud threat landscapes. Furthermore, its graph-based reasoning enhances interpretability and supports actionable insights for real-time threat response. Overall, this framework establishes a practical pathway toward intelligent, adaptive, and interpretable intrusion diagnosis in next-generation IoT-cloud ecosystems.
{"title":"A multi-stage framework for scalable and context-aware intrusion detection in IoT-cloud systems using deep latent modeling and graph-based attack classification","authors":"Rajakumar Ponnumani , Nisha Vasudeva , Thenmozhi Elumalai , Prabu Kaliyaperumal , Balamurugan Balusamy , Francesco Benedetto","doi":"10.1016/j.compeleceng.2026.110949","DOIUrl":"10.1016/j.compeleceng.2026.110949","url":null,"abstract":"<div><div>The rapid proliferation of Internet of Things (IoT) devices in cloud environments has led to an expanded attack surface and increased susceptibility to diverse and evolving cyber threats. This study proposes a robust, multi-stage hybrid intrusion detection framework designed to address the challenges of high-dimensional data, class imbalance, and dynamic traffic in IoT ecosystems. The framework integrates Variational AutoEncoder (VAE) for latent feature compression, Isolation Forest (IF) for unsupervised anomaly detection, and Graph Attention Network (GAT) for relational modeling and multi-class classification. The CIC IoT-DIAD 2024 dataset is utilized to evaluate performance across multiple attack categories. The VAE extracts compact latent representations, enabling effective anomaly detection through IF. Detected anomalies are then structured into graph topologies, and classified by GAT based on node-level features and inter-node relations. Experimental results demonstrate superior detection performance with an overall accuracy of 99.08% and an F1-score of 98.03%, outperforming traditional and deep learning baselines. The proposed system exhibits strong scalability, generalization, and adaptability to dynamic IoT-cloud threat landscapes. Furthermore, its graph-based reasoning enhances interpretability and supports actionable insights for real-time threat response. Overall, this framework establishes a practical pathway toward intelligent, adaptive, and interpretable intrusion diagnosis in next-generation IoT-cloud ecosystems.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110949"},"PeriodicalIF":4.9,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Autism Spectrum Disorder (ASD) affects approximately 1% of the global child population, yet current gold-standard diagnostic methods remain time-intensive and expertise-dependent. Electroencephalography (EEG) offers an objective and scalable approach for neurophysiological measurement, facilitating early detection.
Methods
This study evaluated three neural sequence architectures —Long Short-Term Memory (LSTM), Transformer, and Mamba (Selective State Space Model) —for ASD classification using 47-channel, 150-second resting-state EEG recordings from 56 adults (28 with ASD, 28 controls) from the University of Sheffield dataset. Data were preprocessed using MNE-Python with band-pass filtering (0.50–50 Hz), Independent Component Analysis (ICA) artifact removal, and z-score normalization. Models were trained on epochs of varying durations (1 s, 2.50 s, 5 s) using stratified 5-fold cross-validation, with performance evaluated on a held-out test set (15%). Mixture-of-Experts (MoE) ensembles were constructed using performance-based weighted averaging. Regional classification and spectral analyses identified anatomical and frequency-specific biomarkers.
Results
The Mamba model achieved 98.18% accuracy with only 2972 parameters and a training time of 0.09 min at 2.50-second epochs. LSTM (144,578 parameters) reached 95.25% accuracy, while Transformer (38,946 parameters) attained 94.41%. The optimal Mamba+LSTM ensemble achieved 98.46% accuracy (Cohen's κ=0.97, ROC-AUC=99.84%) with only 11 misclassifications from 716 test samples. Regional analysis revealed frontal lobe dominance (76.81% accuracy, 25 channels) with theta-band (4–8 Hz) biomarkers. Spectral analysis confirmed characteristic ASD patterns: elevated delta/theta power, suppressed alpha rhythm, and increased beta/gamma activity. Single-channel analysis identified C5 (left central, 58.80% accuracy) as the most discriminative electrode.
Conclusions
Neural sequence models, particularly the parameter-efficient Mamba architecture and the Mamba+LSTM ensemble, demonstrate exceptional performance for EEG-based ASD classification, offering a clinically scalable and objective diagnostic tool. The frontal-central electrode configuration and theta-band biomarkers provide neurophysiologically interpretable features suitable for portable EEG systems and early screening applications.
{"title":"Quantitative EEG-based autism spectrum disorder detection using neural sequence models","authors":"Majid Nour , Ümit Şentürk , Alperen Akgül , Kemal Polat","doi":"10.1016/j.compeleceng.2026.110962","DOIUrl":"10.1016/j.compeleceng.2026.110962","url":null,"abstract":"<div><h3>Background</h3><div>Autism Spectrum Disorder (ASD) affects approximately 1% of the global child population, yet current gold-standard diagnostic methods remain time-intensive and expertise-dependent. Electroencephalography (EEG) offers an objective and scalable approach for neurophysiological measurement, facilitating early detection.</div></div><div><h3>Methods</h3><div>This study evaluated three neural sequence architectures —Long Short-Term Memory (LSTM), Transformer, and Mamba (Selective State Space Model) —for ASD classification using 47-channel, 150-second resting-state EEG recordings from 56 adults (28 with ASD, 28 controls) from the University of Sheffield dataset. Data were preprocessed using MNE-Python with band-pass filtering (0.50–50 Hz), Independent Component Analysis (ICA) artifact removal, and z-score normalization. Models were trained on epochs of varying durations (1 s, 2.50 s, 5 s) using stratified 5-fold cross-validation, with performance evaluated on a held-out test set (15%). Mixture-of-Experts (MoE) ensembles were constructed using performance-based weighted averaging. Regional classification and spectral analyses identified anatomical and frequency-specific biomarkers.</div></div><div><h3>Results</h3><div>The Mamba model achieved 98.18% accuracy with only 2972 parameters and a training time of 0.09 min at 2.50-second epochs. LSTM (144,578 parameters) reached 95.25% accuracy, while Transformer (38,946 parameters) attained 94.41%. The optimal Mamba+LSTM ensemble achieved 98.46% accuracy (Cohen's κ=0.97, ROC-AUC=99.84%) with only 11 misclassifications from 716 test samples. Regional analysis revealed frontal lobe dominance (76.81% accuracy, 25 channels) with theta-band (4–8 Hz) biomarkers. Spectral analysis confirmed characteristic ASD patterns: elevated delta/theta power, suppressed alpha rhythm, and increased beta/gamma activity. Single-channel analysis identified C5 (left central, 58.80% accuracy) as the most discriminative electrode.</div></div><div><h3>Conclusions</h3><div>Neural sequence models, particularly the parameter-efficient Mamba architecture and the Mamba+LSTM ensemble, demonstrate exceptional performance for EEG-based ASD classification, offering a clinically scalable and objective diagnostic tool. The frontal-central electrode configuration and theta-band biomarkers provide neurophysiologically interpretable features suitable for portable EEG systems and early screening applications.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110962"},"PeriodicalIF":4.9,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-09DOI: 10.1016/j.compeleceng.2026.110950
Rong Zhou
This study conducts cryptanalysis on a Novel Image Cryptosystem based on Latin Squares (NIC-LS). The NIC-LS adopts a multi-round encryption structure, with row or column scrambling alternating with diffusion. It leverages properties of Latin squares generated by the Coupled Map Lattice (CML) system to determine scrambling/diffusion selection modes, aiming for enhanced encryption performance. However, all diffusion operations in NIC-LS rely solely on simple modular addition—this flaw gives rise to an equivalent algorithm for the cryptosystem. When a Differential Attack (DA) is applied to this equivalent scheme, the system degenerates into a linear one: all diffusion effects are eliminated, leaving only the scrambling component. Building on the superposition principle and standard orthogonal basis concept, this study further breaks the equivalent algorithm (and thus NIC-LS) via a Chosen-Ciphertext Attack (CCA). Notably, the attack’s computational complexity is extremely low and some countermeasures are discussed based on the cryptanalysis. Both theoretical analysis and experimental results confirm the proposed cryptanalysis is effective and practically feasible.
{"title":"Cryptanalysis of an image encryption algorithm using Latin squares","authors":"Rong Zhou","doi":"10.1016/j.compeleceng.2026.110950","DOIUrl":"10.1016/j.compeleceng.2026.110950","url":null,"abstract":"<div><div>This study conducts cryptanalysis on a Novel Image Cryptosystem based on Latin Squares (NIC-LS). The NIC-LS adopts a multi-round encryption structure, with row or column scrambling alternating with diffusion. It leverages properties of Latin squares generated by the Coupled Map Lattice (CML) system to determine scrambling/diffusion selection modes, aiming for enhanced encryption performance. However, all diffusion operations in NIC-LS rely solely on simple modular addition—this flaw gives rise to an equivalent algorithm for the cryptosystem. When a Differential Attack (DA) is applied to this equivalent scheme, the system degenerates into a linear one: all diffusion effects are eliminated, leaving only the scrambling component. Building on the superposition principle and standard orthogonal basis concept, this study further breaks the equivalent algorithm (and thus NIC-LS) via a Chosen-Ciphertext Attack (CCA). Notably, the attack’s computational complexity is extremely low and some countermeasures are discussed based on the cryptanalysis. Both theoretical analysis and experimental results confirm the proposed cryptanalysis is effective and practically feasible.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110950"},"PeriodicalIF":4.9,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-09DOI: 10.1016/j.compeleceng.2025.110932
Marriam Liaqat , Ali Raza , Muhammad Sajid Iqbal , Muhammad Adnan , Usman Abbasi , Maqsood Khan
The super smart grid (SSG) is a revolutionary grid which offers significant fossil fuel elimination, emissions reduction, renewable energy integration, and demand fulfillment. However, such mega grids are in the strategic analysis stage due to the involvement of multiple countries and complexities. Although the existing literature has performed different types of analysis for the different SSGs around the world, there is a lack of studies on the strategic analysis of the SSG planned by the South Asian Association for Regional Cooperation (SAARC). For the first time, this review paper presents the hybrid PESTEL-SWOT analysis for the futuristic SAARC SSG. This paper offers important insights and strategies for the implementation of the futuristic SAARC SSG. For instance, a practical strategy towards the emergence of the SAARC SSG is the encouragement of the P2P trading at a very basic level through the hierarchical integration of thousands of prosumers, prosumer communities, and national grids.
{"title":"Empowering SAARC's energy future: A PESTEL-SWOT roadmap for super smart grids and P2P energy trading","authors":"Marriam Liaqat , Ali Raza , Muhammad Sajid Iqbal , Muhammad Adnan , Usman Abbasi , Maqsood Khan","doi":"10.1016/j.compeleceng.2025.110932","DOIUrl":"10.1016/j.compeleceng.2025.110932","url":null,"abstract":"<div><div>The super smart grid (SSG) is a revolutionary grid which offers significant fossil fuel elimination, emissions reduction, renewable energy integration, and demand fulfillment. However, such mega grids are in the strategic analysis stage due to the involvement of multiple countries and complexities. Although the existing literature has performed different types of analysis for the different SSGs around the world, there is a lack of studies on the strategic analysis of the SSG planned by the South Asian Association for Regional Cooperation (SAARC). For the first time, this review paper presents the hybrid PESTEL-SWOT analysis for the futuristic SAARC SSG. This paper offers important insights and strategies for the implementation of the futuristic SAARC SSG. For instance, a practical strategy towards the emergence of the SAARC SSG is the encouragement of the P2P trading at a very basic level through the hierarchical integration of thousands of prosumers, prosumer communities, and national grids.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110932"},"PeriodicalIF":4.9,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-09DOI: 10.1016/j.compeleceng.2026.110964
Jasmita Malik, Raja Muthalagu, Pranav M. Pawar, Mithun Mukherjee
Adversarial machine learning (AML) attacks including evasion, poisoning, and privacy-targeting techniques represent a new class of evolving threats to AI systems. However, traditional cyber risk quantification approaches struggle to capture the uncertainty and impact of such dynamic threats. This study introduces a novel framework to quantify cyber risk exposure and business impact stemming from new-age AML attacks. Leveraging Monte Carlo simulations, the framework models probabilistic loss distributions based on attack likelihoods and impact ranges. Applied to a ransomware attack scenario on a machine learning system, the framework estimates an Annualized Loss Expectancy of approximately $1.6 million to an organization, revealing the potential for unexpected heavy-tail, high-cost outcomes. The framework is further validated across diverse adversarial scenarios, including evasion, poisoning, and privacy attacks. The results provide decision-makers with a structured way to assess control effectiveness and prioritize cybersecurity investments using quantitative metrics. This work bridges the gap between technical threat intelligence and strategic cybersecurity investment financial planning, offering a practical path toward resilient and secure deployment of AI systems in organizations.
{"title":"Cyber risk quantification for adversarial machine learning attacks","authors":"Jasmita Malik, Raja Muthalagu, Pranav M. Pawar, Mithun Mukherjee","doi":"10.1016/j.compeleceng.2026.110964","DOIUrl":"10.1016/j.compeleceng.2026.110964","url":null,"abstract":"<div><div>Adversarial machine learning (AML) attacks including evasion, poisoning, and privacy-targeting techniques represent a new class of evolving threats to AI systems. However, traditional cyber risk quantification approaches struggle to capture the uncertainty and impact of such dynamic threats. This study introduces a novel framework to quantify cyber risk exposure and business impact stemming from new-age AML attacks. Leveraging Monte Carlo simulations, the framework models probabilistic loss distributions based on attack likelihoods and impact ranges. Applied to a ransomware attack scenario on a machine learning system, the framework estimates an Annualized Loss Expectancy of approximately $1.6 million to an organization, revealing the potential for unexpected heavy-tail, high-cost outcomes. The framework is further validated across diverse adversarial scenarios, including evasion, poisoning, and privacy attacks. The results provide decision-makers with a structured way to assess control effectiveness and prioritize cybersecurity investments using quantitative metrics. This work bridges the gap between technical threat intelligence and strategic cybersecurity investment financial planning, offering a practical path toward resilient and secure deployment of AI systems in organizations.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110964"},"PeriodicalIF":4.9,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-09DOI: 10.1016/j.compeleceng.2025.110931
Qiyuan Gao, Qianhong Wu, Qi Liu, Junxiang Nong
Verifiable computation is essential for ensuring correctness in decentralized systems, yet existing approaches rely heavily on circuit-based proofs, task decomposition, or trusted hardware, which introduce high overhead and limit generality. To address these challenges, we propose CleVer, a compute-and-leave anonymous verification framework for general-purpose computation.
CleVer avoids circuit-based proof generation by using snapshot-based state transitions, enabling single-step dispute resolution without task decomposition. We design a cumulative staking incentive mechanism that guarantees profitability for honest verifiers and enforces bounded finality under adversarial budgets. Furthermore, we introduce an anonymous verifier protocol to prevent targeted attacks and collusion. Security is analyzed under a formal threat model, and experiments demonstrate that CleVer significantly reduces verification rounds and on-chain burden compared with existing optimistic-verification frameworks. Our results show that CleVer provides an efficient, incentive-aligned, and privacy-preserving foundation for scalable off-chain computation.
{"title":"CleVer: A compute-and-leave anonymous verification framework for general purpose computation","authors":"Qiyuan Gao, Qianhong Wu, Qi Liu, Junxiang Nong","doi":"10.1016/j.compeleceng.2025.110931","DOIUrl":"10.1016/j.compeleceng.2025.110931","url":null,"abstract":"<div><div>Verifiable computation is essential for ensuring correctness in decentralized systems, yet existing approaches rely heavily on circuit-based proofs, task decomposition, or trusted hardware, which introduce high overhead and limit generality. To address these challenges, we propose CleVer, a compute-and-leave anonymous verification framework for general-purpose computation.</div><div>CleVer avoids circuit-based proof generation by using snapshot-based state transitions, enabling single-step dispute resolution without task decomposition. We design a cumulative staking incentive mechanism that guarantees profitability for honest verifiers and enforces bounded finality under adversarial budgets. Furthermore, we introduce an anonymous verifier protocol to prevent targeted attacks and collusion. Security is analyzed under a formal threat model, and experiments demonstrate that CleVer significantly reduces verification rounds and on-chain burden compared with existing optimistic-verification frameworks. Our results show that CleVer provides an efficient, incentive-aligned, and privacy-preserving foundation for scalable off-chain computation.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110931"},"PeriodicalIF":4.9,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-08DOI: 10.1016/j.compeleceng.2026.110970
Wanna Cui, Hak-Keung Lam
Recommender systems play an essential role in alleviating information overload by delivering personalized suggestions to users across domains such as e-commerce, restaurant services, and digital media. In recent years, graph-based approaches, particularly those leveraging graph convolutional networks (GCNs), have shown strong performance by modeling high-order connectivity. However, their effectiveness remains constrained by three critical challenges: the sparsity of user–item interactions, the presence of noisy or transient behaviors that distort preference modeling, and the underutilization of contextual information contained in reviews and product descriptions. To address these limitations, we propose a novel framework, termed fuzzy and variable weight graph convolutional network (FVW-GCN). The framework incorporates a fuzzy relation modeling module that enriches the adjacency structure by applying fuzzy C-means clustering to semantic embeddings extracted from pre-trained language models, thereby improving connectivity for sparse and long-tail items. In addition, a variable-weight GCN module is introduced, where a tuning GCN learns localized weight matrices from sampled subgraphs, which are then used by a tuned GCN to adaptively refine embeddings and suppress noisy signals. Through this combination, FVW-GCN effectively strengthens meaningful relations while reducing the influence of unreliable interactions. Extensive experiments conducted on benchmark datasets demonstrate that FVW-GCN consistently outperforms state-of-the-art baselines across several standard evaluation metrics, including recall, normalized discounted cumulative gain, and hit ratio. These results confirm the robustness and effectiveness of the proposed framework, highlighting its potential to support more accurate, diverse, and user-centric recommendation services in real-world applications.
{"title":"Fuzzy-enhanced variable weight graph convolutional networks for recommender systems","authors":"Wanna Cui, Hak-Keung Lam","doi":"10.1016/j.compeleceng.2026.110970","DOIUrl":"10.1016/j.compeleceng.2026.110970","url":null,"abstract":"<div><div>Recommender systems play an essential role in alleviating information overload by delivering personalized suggestions to users across domains such as e-commerce, restaurant services, and digital media. In recent years, graph-based approaches, particularly those leveraging graph convolutional networks (GCNs), have shown strong performance by modeling high-order connectivity. However, their effectiveness remains constrained by three critical challenges: the sparsity of user–item interactions, the presence of noisy or transient behaviors that distort preference modeling, and the underutilization of contextual information contained in reviews and product descriptions. To address these limitations, we propose a novel framework, termed fuzzy and variable weight graph convolutional network (FVW-GCN). The framework incorporates a fuzzy relation modeling module that enriches the adjacency structure by applying fuzzy C-means clustering to semantic embeddings extracted from pre-trained language models, thereby improving connectivity for sparse and long-tail items. In addition, a variable-weight GCN module is introduced, where a tuning GCN learns localized weight matrices from sampled subgraphs, which are then used by a tuned GCN to adaptively refine embeddings and suppress noisy signals. Through this combination, FVW-GCN effectively strengthens meaningful relations while reducing the influence of unreliable interactions. Extensive experiments conducted on benchmark datasets demonstrate that FVW-GCN consistently outperforms state-of-the-art baselines across several standard evaluation metrics, including recall, normalized discounted cumulative gain, and hit ratio. These results confirm the robustness and effectiveness of the proposed framework, highlighting its potential to support more accurate, diverse, and user-centric recommendation services in real-world applications.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110970"},"PeriodicalIF":4.9,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-08DOI: 10.1016/j.compeleceng.2026.110965
Rong Zhou
This study presents a cryptanalysis of a dynamic image cryptosystem based on chaos, referred to as DIC-BOC. Using DIC-BOC as a case study, the work introduces an innovative concept — termed T-ADTC (Thought of Applying Database to Cryptanalysis) — specifically designed to mount attacks against various instances of DIC-BOC. The particular DIC-BOC under investigation is an enhanced version of a plaintext-independent cryptosystem, featuring two key improvements to its dynamic mechanism: (1) linking the chaotic sequence used for encryption directly to the plaintext during the permutation stage, and (2) incorporating dynamic ciphertext feedback into the diffusion process. These enhancements significantly boost security compared to the original scheme. Although the authors assert the robustness of DIC-BOC based on empirical tests, rigorous cryptanalysis reveals critical vulnerabilities that render it susceptible to the proposed T-ADTC attack. Guided by T-ADTC, the study further refines this specific DIC-BOC, achieving additional advancements. Moreover, T-ADTC is not limited to this instance; it can be generalized to evaluate other DIC-BOC variants and offers crucial insights for the future development of cryptographic systems. Both theoretical analysis and experimental results confirm the feasibility and effectiveness of the proposed approach.
{"title":"Attack a class of dynamic cryptosystem based on chaos","authors":"Rong Zhou","doi":"10.1016/j.compeleceng.2026.110965","DOIUrl":"10.1016/j.compeleceng.2026.110965","url":null,"abstract":"<div><div>This study presents a cryptanalysis of a dynamic image cryptosystem based on chaos, referred to as DIC-BOC. Using DIC-BOC as a case study, the work introduces an innovative concept — termed T-ADTC (Thought of Applying Database to Cryptanalysis) — specifically designed to mount attacks against various instances of DIC-BOC. The particular DIC-BOC under investigation is an enhanced version of a plaintext-independent cryptosystem, featuring two key improvements to its dynamic mechanism: (1) linking the chaotic sequence used for encryption directly to the plaintext during the permutation stage, and (2) incorporating dynamic ciphertext feedback into the diffusion process. These enhancements significantly boost security compared to the original scheme. Although the authors assert the robustness of DIC-BOC based on empirical tests, rigorous cryptanalysis reveals critical vulnerabilities that render it susceptible to the proposed T-ADTC attack. Guided by T-ADTC, the study further refines this specific DIC-BOC, achieving additional advancements. Moreover, T-ADTC is not limited to this instance; it can be generalized to evaluate other DIC-BOC variants and offers crucial insights for the future development of cryptographic systems. Both theoretical analysis and experimental results confirm the feasibility and effectiveness of the proposed approach.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110965"},"PeriodicalIF":4.9,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In real-world, numerous leaf diseases are proliferating due to soil pollution and weather-related factors. Manual identification is slow and often ineffective. Identification hazards are created when noisy data and binary class imbalance problems are present. To address the noise and imbalanced data issue, several affinity and class probability-models were suggested, which reduce noise through regularization and handles class imbalance using affinity values from support vector data description (SVDD) and class probabilities from k-nearest neighbour (KNN). Minority samples with low affinity and probability receive less weight, while majority samples with higher values strongly influence the decision boundary. To enhance generalization an computational efficiency, an affinity and class probability-based fuzzy random vector functional link network (ACFRVFL) is introduced, combining fuzzy logic, SVDD, and KNN with RVFL. Moreover, an affinity and class probability-based fuzzy twin RVFL (ACFTRVFL) model is also suggested for improved performance. The study evaluates performance using various benchmark datasets.
{"title":"Affinity-based fuzzy twin random vector functional link network classifier","authors":"Chittabarni Sarkar , Deepak Gupta , Rajat Subhra Goswami , Barenya Bikash Hazarika","doi":"10.1016/j.compeleceng.2025.110923","DOIUrl":"10.1016/j.compeleceng.2025.110923","url":null,"abstract":"<div><div>In real-world, numerous leaf diseases are proliferating due to soil pollution and weather-related factors. Manual identification is slow and often ineffective. Identification hazards are created when noisy data and binary class imbalance problems are present. To address the noise and imbalanced data issue, several affinity and class probability-models were suggested, which reduce noise through regularization and handles class imbalance using affinity values from support vector data description (SVDD) and class probabilities from k-nearest neighbour (KNN). Minority samples with low affinity and probability receive less weight, while majority samples with higher values strongly influence the decision boundary. To enhance generalization an computational efficiency, an affinity and class probability-based fuzzy random vector functional link network (ACFRVFL) is introduced, combining fuzzy logic, SVDD, and KNN with RVFL. Moreover, an affinity and class probability-based fuzzy twin RVFL (ACFTRVFL) model is also suggested for improved performance. The study evaluates performance using various benchmark datasets.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110923"},"PeriodicalIF":4.9,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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